《自然語言處理技術》代碼42所示。代碼STYLEREF1\s4SEQ代碼\*ARABIC\s12準備文本數據text_data=["我關注鄉村振興","鄉村振興是一項重要任務","實現鄉村振興面臨著諸多挑戰","我積極支持鄉村振興"]基于Word2Vec進行文本向量化構建Word2Vec模型，并使用Word2Vec模型對文本數據進行向量化處理，如REF_Ref133687894\h代碼43所示。代碼STYLEREF1\s4SEQ代碼\*ARABIC\s13基于Word2Vec進行文本向量化sentences=[simple_preprocess(sentence)forsentenceintext_data]#對文本數據進行簡化預處理model=Word2Vec(sentences,min_count=1)#構建Word2Vec模型model.train(sentences,total_examples=len(sentences),epochs=10)vectorized_data=np.array([model.wv[sentence]forsentenceinsentences])vectorized_data=vectorized_data.reshape(4,100)print("詞向量矩陣：\n",vectorized_data)運行REF_Ref133687894\h代碼43，得到詞向量矩陣如下。詞向量矩陣：[[-8.2426788e-039.2993546e-03-1.9766092e-04-1.9672776e-034.6036290e-03-4.0953159e-032.7431131e-036.9399667e-036.0654259e-03-7.5107957e-039.3823504e-034.6718074e-033.9661191e-03-6.2435055e-038.4599778e-03-2.1501661e-038.8251876e-03-5.3620026e-03-8.1294207e-036.8245577e-031.6711927e-03-2.1985101e-039.5135998e-039.4938539e-03-9.7740479e-032.5052286e-036.1566923e-033.8724565e-03[[-5.3622725e-042.3643016e-045.1033497e-039.0092728e-03-9.3029495e-03-7.1168090e-036.4588715e-038.9729885e-03-5.0154282e-03-3.7633730e-037.3805046e-03-1.5334726e-03-4.5366143e-036.5540504e-03-4.8601604e-03-1.8160177e-032.8765798e-039.9187379e-04-8.2852151e-03-9.4488189e-037.3117660e-035.0702621e-036.7576934e-037.6286553e-046.3508893e-03-3.4053659e-03-9.4640255e-045.7685734e-03-7.5216386e-03-3.9361049e-03-7.5115822e-03-9.3004224e-049.5381187e-03-7.3191668e-03-2.3337698e-03-1.9377422e-038.0774352e-03-5.9308959e-034.5161247e-05-4.7537349e-03-9.6035507e-035.0072931e-03-8.7595871e-03-4.3918253e-03-3.5099984e-05-2.9618264e-04-7.6612402e-039.6147414e-034.9820566e-039.2331432e-03-8.1579182e-034.4957972e-03-4.1370774e-038.2453492e-048.4986184e-03-4.4621779e-034.5175003e-03-6.7869616e-03-3.5484887e-039.3985079e-03-1.5776539e-033.2137157e-04-4.1406299e-03-7.6826881e-03-1.5080094e-032.4697948e-03-8.8802812e-045.5336617e-03-2.7429771e-032.2600652e-035.4557943e-038.3459523e-03-1.4537406e-03-9.2081428e-034.3705511e-035.7178497e-047.4419067e-03-8.1328390e-04-2.6384138e-03-8.7530091e-03-8.5655687e-042.8265619e-035.4014279e-037.0526553e-03-5.7031228e-031.8588186e-036.0888622e-03-4.7980524e-03-3.1072616e-036.7976285e-031.6314745e-031.8991709e-043.4736372e-032.1777629e-049.6188262e-035.0606038e-03-8.9173913e-03-7.0415614e-039.0145587e-046.3925339e-03]]計算文本相似度并可視化處理使用余弦距離計算文本之間的相似度，并使用熱力圖可視化文本之間的相似度，如REF_Ref133687041\h代碼44所示。代碼STYLEREF1\s4SEQ代碼\*ARABIC\s14計算文本相似度并可視化處理#計算每一行之間的余弦距離distances=pdist(vectorized_data,metric='cosine')#將距離轉換為方陣形式similarity_matrix=np.zeros((vectorized_data.shape[0],vectorized_data.shape[0]))similarity_matrix[np.triu_indices(vectorized_data.shape[0],k=1)]=distancesplt.imshow(similarity_matrix,cmap='viridis',interpolation='nearest')#繪制熱力圖plt.colorbar()#添加顏色條plt.xticks(range(len(text_data)),text_data,rotation=90)#設置x軸刻度標簽plt.yticks(range(len(text_data)),text_data)#設置y軸刻度標簽plt.title("文本相似度")#設置標題plt.xlabel("文本")#